Abstract: Aspects of data modification for neural networks are described herein. The aspects may include a data modifier configured to receive input data and weight values of a neural network. The data modifier may include an input data configured to modify the received input data and a weight modifier configured to modify the received weight values. The aspects may further include a computing unit configured to calculate one or more groups of output data based on the modified input data and the modifier weight values.

Abstract: The present disclosure provides a processing device for performing generative adversarial network and a method for machine creation applying the processing device. The processing device includes a memory configured to receive input data including a random noise and reference data, and store a discriminator neural network parameter and a generator neural network parameter, and the processing device further includes a computation device configured to transmit the random noise input data into a generator neural network and perform operation to obtain a noise generation result, and input both of the noise generation result and the reference data into a discriminator neural network and perform operation to obtain a discrimination result, and further configured to update the discriminator neural network parameter and the generator neural network parameter according to the discrimination result.

Abstract: The present disclosure provides a processing device including: a coarse-grained pruning unit configured to perform coarse-grained pruning on a weight of a neural network to obtain a pruned weight, an operation unit configured to train the neural network according to the pruned weight. The coarse-grained pruning unit is specifically configured to select M weights from the weights of the neural network through a sliding window, and when the M weights meet a preset condition, all or part of the M weights may be set to 0. The processing device can reduce the memory access while reducing the amount of computation, thereby obtaining an acceleration ratio and reducing energy consumption.

Abstract: A mechanism is provided in a data processing system for predictive and prescriptive analytics for managing high-cost claimants. The mechanism trains a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data. The mechanism applies transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model. The mechanism then applies the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data. The mechanism generates association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants and applies the association rules to the second set of customized client data to generate a set of recommendations.

Abstract: The disclosure provides an information processing device and method. The information processing device includes a storage module a storage module configured to acquire information data, wherein the information data including at least one key feature and the storage module pre-storing true confidence corresponding to the key feature; an operational circuit configured to determine predicted confidence corresponding to the key feature according to the information data and judge whether the predicted confidence of the key feature exceeds a preset threshold value range of the true confidence corresponding to the key feature or not; a controlling circuit configured to control the storage module to modify the key feature or send out a modification signal to the outside when the predicted confidence exceeds the preset threshold value of the true confidence. The information processing device of the disclosure can automatically correct and modify handwriting, text, image or video actions instead of artificial method.

Abstract: An example method of determining size of virtual last-level cache (LLC) exposed to a virtual machine (VM) supported by a hypervisor executing on a host computer includes: obtaining, by the hypervisor, a host topology of the host computer, the host topology including a number of LLCs in a central processing unit (CPU) of the host computer and a host LLC size being a size of each of the LLCs in the CPU; obtaining, by the hypervisor, a virtual socket size for a virtual socket presented to the VM by the hypervisor and a virtual non-uniform memory access (NUMA) node size presented to the VM by the hypervisor; determining, by the hypervisor, a virtual LLC size for the VM based on the host topology, the virtual socket size, the virtual NUMA node size, and a plurality of constraints; and presenting, to the VM, the virtual LLC size in processor feature discovery information.

Abstract: Techniques for optimizing virtual machine (VM) scheduling on a non-uniform cache access (NUCA) system are provided. In one set of embodiments, a hypervisor of the NUCA system can partition the virtual CPUs of each VM running on the system into logical constructs referred to as last level cache (LLC) groups, where each LLC group is sized to match (or at least not exceed) the LLC domain size of the system. The hypervisor can then place/load balance the virtual CPUs of each VM on the system’s cores in a manner that attempts to keep virtual CPUs which are part of the same LLC group within the same LLC domain, subject to various factors such as compute load, cache contention, and so on.

Abstract: The application provides an information processing device, system and method. The information processing device mainly includes a storage module and a data processing module, where the storage module is configured to receive and store input data, instruction and output data, and the input data includes one or more key features; the data processing module is configured to identify the key features included in the input data and score the input data in the storage module according to a judgment result. The information processing device, system and method provided by the application automatically scores text, pictures, audio, video, and the like instead of manually scoring, which is more accurate and faster.

Abstract: The present invention includes a method for thermodynamic modeling of asphaltene precipitation comprising: calculating the Gibbs free energy for the transition between asphaltene molecules in solution into an imaginary crystalline asphaltene nanoaggregates or asphaltene nanocrystals; calculating the Gibbs free energy for the transition between asphaltene nanoaggregates or nanocrystals redissolving into colloidal asphaltene nanoaggregates using the computer: and predicting asphaltene solubility in a solvent, wherein the predicted asphaltene solubility is used to add a solvent to a liquid, semi-solid, or solid comprising asphaltenes to prevent, e.g., fouling of a wellbore, pipeline, downstream unit operations, to provide flow assurance for crude oil pipeline network, or for petroleum crude blending.

Abstract: The present disclosure provides a data screening device and method, which employ a storage unit and a register unit, and are capable of performing operations on data of different storage structures and different sizes efficiently.

Abstract: The present disclosure provides a processing device for performing generative adversarial network and a method for machine creation applying the processing device. The processing device includes a memory configured to receive input data including a random noise and reference data, and store a discriminator neural network parameter and a generator neural network parameter, and the processing device further includes a computation device configured to transmit the random noise input data into a generator neural network and perform operation to obtain a noise generation result, and input both of the noise generation result and the reference data into a discriminator neural network and perform operation to obtain a discrimination result, and further configured to update the discriminator neural network parameter and the generator neural network parameter according to the discrimination result.

Abstract: The present disclosure provides a processing device including: a coarse-grained pruning unit configured to perform coarse-grained pruning on a weight of a neural network to obtain a pruned weight, an operation unit configured to train the neural network according to the pruned weight. The coarse-grained pruning unit is specifically configured to select M weights from the weights of the neural network through a sliding window, and when the M weights meet a preset condition, all or part of the M weights may be set to 0. The processing device can reduce the memory access while reducing the amount of computation, thereby obtaining an acceleration ratio and reducing energy consumption.

Abstract: The disclosure provides an information processing device and method. The information processing device includes a storage module a storage module configured to acquire information data, wherein the information data including at least one key feature and the storage module pre-storing true confidence corresponding to the key feature; an operational circuit configured to determine predicted confidence corresponding to the key feature according to the information data and judge whether the predicted confidence of the key feature exceeds a preset threshold value range of the true confidence corresponding to the key feature or not; a controlling circuit configured to control the storage module to modify the key feature or send out a modification signal to the outside when the predicted confidence exceeds the preset threshold value of the true confidence. The information processing device of the disclosure can automatically correct and modify handwriting, text, image or video actions instead of artificial method.

Abstract: The application provides an information processing device, system and method. The information processing device mainly includes a storage module and a data processing module, where the storage module is configured to receive and store input data, instruction and output data, and the input data includes one or more key features; the data processing module is configured to identify the key features included in the input data and score the input data in the storage module according to a judgment result. The information processing device, system and method provided by the application automatically scores text, pictures, audio, video, and the like instead of manually scoring, which is more accurate and faster.

Abstract: The present disclosure provides a data screening device and method, which employ a storage unit and a register unit, and are capable of performing operations on data of different storage structures and different sizes efficiently.

Abstract: Aspects of activation function computation for neural networks are described herein. The aspects may include a search module configured to receive an input value. The search module may be further configured to identify a data range based on the received input value and an index associated with the data range. Meanwhile, a count value may be set to one. Further, the search module may be configured to identify a slope value and an intercept value that correspond to the input value. A computation module included in the aspects may be configured to calculate an output value based on the slope value, the intercept value and the input value. In at least some examples, the process may be repeated to increase the accuracy of the result until the count of the repetition reaches the identified index.

Abstract: Aspects of data modification for neural networks are described herein. The aspects may include a data modifier configured to receive input data and weight values of a neural network. The data modifier may include an input data configured to modify the received input data and a weight modifier configured to modify the received weight values. The aspects may further include a computing unit configured to calculate one or more groups of output data based on the modified input data and the modifier weight values.

Abstract: The present disclosure provides an operation device, comprising: an operation module for executing a neural network operation; and a power conversion module connected to the operation module, for converting input neuron data and/or output neuron data of the neural network operation into power neuron data. The present disclosure further provides an operation method. The operation device and method according to the present disclosure reduce the cost of storage resources and computing resources and increase the operation speed.

Abstract: The present invention includes a method for thermodynamic modeling of asphaltene precipitation comprising: calculating the Gibbs free energy for the transition between asphaltene molecules in solution into an imaginary crystalline asphaltene nanoaggregates or asphaltene nanocrystals; calculating the Gibbs free energy for the transition between asphaltene nanoaggregates or nanocrystals redissolving into colloidal asphaltene nanoaggregates using the computer: and predicting asphaltene solubility in a solvent, wherein the predicted asphaltene solubility is used to add a solvent to a liquid, semi-solid, or solid comprising aspahaltenes to prevent, e.g., fouling of a wellbore, pipeline, downstream unit operations, to provide flow assurance for crude oil pipeline network, or for petroleum crude blending.